{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c3f8a08c-f4b9-4e62-a365-98badf521854",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "88c54531-1a7e-49ae-864f-25201fbd4a43",
   "metadata": {},
   "outputs": [],
   "source": [
    "newsguard = pd.read_csv('/net/lazer/lab-lazer/shared_projects/datasets/newsguard/metadata/2023-05.csv')\n",
    "newsguard.rename(columns={\"Domain\": \"expanded_domain\"}, inplace=True)\n",
    "newsguard = newsguard[~newsguard['Rating'].isin(['P', 'S'])]\n",
    "newsguard = newsguard.drop_duplicates(subset=['expanded_domain'], keep='first').reset_index(drop=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d68c3e4b-9012-4108-b18c-5d4f2e1ccab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "newsguard.to_csv('newsguard_ratings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5566744c-53f6-4f24-b8fb-0ab381cb065b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_932774/2212451493.py:1: DtypeWarning: Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv('/home/ozturan/covid-conspiracy-narratives/src/musk/data/deca_mis_conc_with_shortener3.tsv', sep= '\\t')\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('/home/ozturan/covid-conspiracy-narratives/src/musk/data/deca_mis_conc_with_shortener3.tsv', sep= '\\t')\n",
    "domain_ratings = pd.read_csv('/home/ozturan/covid-conspiracy-narratives/src/domain_ratings_with_shorteners.csv')\n",
    "domain_ratings  = domain_ratings.drop(columns=['Unnamed: 0'])#df = df.drop(columns=['Unnamed: 0'])\n",
    "df_deduplicated  = df.drop_duplicates(subset=['date','expanded_domain']).reset_index(drop=True)\n",
    "merged_df = pd.merge(df, domain_ratings, on='expanded_domain', how='inner')\n",
    "merged_df = pd.merge(merged_df, newsguard, on='expanded_domain', how='inner')\n",
    "\n",
    "merged_df['count'] = merged_df['count'].astype(int) #df['count'] = df['count'].astype(int)\n",
    "merged_df = merged_df.drop_duplicates(subset=['date','expanded_domain']).reset_index(drop=True)\n",
    "# Select specific columns\n",
    "merged_df = merged_df[['date', 'expanded_domain', 'count', 'pc1','Score']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d1a0e920-0c0a-4d47-ae7e-216d19f42ef4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_932774/3476299929.py:26: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  info_qual_decahose['month_year'] = info_qual_decahose['month_year'].dt.to_timestamp()\n"
     ]
    }
   ],
   "source": [
    "\n",
    "top =['youtube.com']\n",
    "# Step 2: Filter the DataFrame to exclude rows with these domains\n",
    "filtered_df = merged_df[~merged_df['expanded_domain'].isin(top)]\n",
    "filtered_df['pc1_weighted_count']=filtered_df['count'] *filtered_df['Score']\n",
    "# Step 3: Convert the 'date' column to datetime format\n",
    "filtered_df['date'] = pd.to_datetime(filtered_df['date'])\n",
    "\n",
    "# Step 4: Group by month and year and calculate the monthly average of 'pc1_weighted_count'\n",
    "filtered_df['month_year'] = filtered_df['date'].dt.to_period('M')\n",
    "\n",
    "# Group by 'month_year' and calculate the sum of 'pc1_weighted_count'\n",
    "pc1_sum = filtered_df.groupby('month_year')['pc1_weighted_count'].sum().reset_index()\n",
    "\n",
    "# Group by 'month_year' and calculate the sum of 'count'\n",
    "count_sum = filtered_df.groupby('month_year')['count'].sum().reset_index()\n",
    "\n",
    "# Merge the two DataFrames based on 'month_year'\n",
    "merged_sums = pd.merge(pc1_sum, count_sum, on='month_year')\n",
    "# Perform the division of 'pc1_weighted_count' sum by 'count' sum\n",
    "result = merged_sums['pc1_weighted_count'] / merged_sums['count']\n",
    "# Add the result to the merged DataFrame\n",
    "merged_sums['weighted_average'] = result\n",
    "# Optionally, keep only relevant columns\n",
    "info_qual_decahose = merged_sums[['month_year', 'weighted_average']]\n",
    "# Convert PeriodDtype to Timestamp\n",
    "info_qual_decahose['month_year'] = info_qual_decahose['month_year'].dt.to_timestamp()\n",
    "info_qual_decahose= info_qual_decahose.iloc[1:-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8b7f20ef-7ad9-4d2a-940b-8bf14f94c19a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "panel = pd.read_csv('/home/ozturan/covid-conspiracy-narratives/src/panel_daily_urls_withbackfilled_andkenny.csv')\n",
    "panel = panel.drop('Unnamed: 0', axis=1)\n",
    "panel_deduplicated  = panel.drop_duplicates(subset=['id']).reset_index(drop=True)\n",
    "panel_merged_df = panel_deduplicated[(panel_deduplicated['date'] >= '2022-01-01') & (panel_deduplicated['date'] < '2023-05-01') ].reset_index(drop=True)\n",
    "\n",
    "panel_merged_df=panel_merged_df.rename(columns={'domain': 'expanded_domain'})\n",
    "\n",
    "# Ensure 'date' is datetime type\n",
    "panel_merged_df['date'] = pd.to_datetime(panel_merged_df['date'])\n",
    "panel_merged_df = panel_merged_df.groupby(['date', 'expanded_domain', 'pc1']).size().reset_index(name='count')\n",
    "panel_merged_df['count'] = panel_merged_df['count'].astype(int)\n",
    "\n",
    "\n",
    "panel_merged_df = pd.merge(panel_merged_df, newsguard, on='expanded_domain', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d0480d5d-3ea7-46cf-a961-8c8bcc63249f",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'panel_merged_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m top6_panel \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124myoutube.com\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124myt.vu\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m----> 2\u001b[0m panel_merged_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpc1_weighted_count\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m=\u001b[39m\u001b[43mpanel_merged_df\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcount\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m*\u001b[39mpanel_merged_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mScore\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Group by 'month_year' and calculate the sum of 'pc1_weighted_count'\u001b[39;00m\n\u001b[1;32m      4\u001b[0m filtered_df \u001b[38;5;241m=\u001b[39m panel_merged_df[\u001b[38;5;241m~\u001b[39mpanel_merged_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mexpanded_domain\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39misin(top6_panel)]\n",
      "\u001b[0;31mNameError\u001b[0m: name 'panel_merged_df' is not defined"
     ]
    }
   ],
   "source": [
    "top6_panel = ['youtube.com','yt.vu']\n",
    "panel_merged_df['pc1_weighted_count']=panel_merged_df['count'] *panel_merged_df['Score']\n",
    "# Group by 'month_year' and calculate the sum of 'pc1_weighted_count'\n",
    "filtered_df = panel_merged_df[~panel_merged_df['expanded_domain'].isin(top6_panel)]\n",
    "filtered_df['month_year'] = filtered_df['date'].dt.to_period('M')\n",
    "pc1_sum = filtered_df.groupby('month_year')['pc1_weighted_count'].sum().reset_index()\n",
    "\n",
    "# Group by 'month_year' and calculate the sum of 'count'\n",
    "count_sum = filtered_df.groupby('month_year')['count'].sum().reset_index()\n",
    "\n",
    "# Merge the two DataFrames based on 'month_year'\n",
    "merged_sums = pd.merge(pc1_sum, count_sum, on='month_year')\n",
    "\n",
    "# Perform the division of 'pc1_weighted_count' sum by 'count' sum\n",
    "merged_sums['weighted_average'] = merged_sums['pc1_weighted_count'] / merged_sums['count']\n",
    "\n",
    "# Optionally, keep only relevant columns\n",
    "info_qual_panel = merged_sums[['month_year', 'weighted_average']]\n",
    "info_qual_panel ['month_year'] = info_qual_panel['month_year'].dt.to_timestamp()\n",
    "# Convert PeriodDtype to Timestamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d8307e1a-9a19-4e5a-aa6c-96e4677472fc",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3905719/1564898668.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  bef_panel['trend'] = intercept + slope * bef_panel.month_year.map(pd.Timestamp.toordinal)\n",
      "/tmp/ipykernel_3905719/1564898668.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  aft_panel['trend'] = intercept + slope * aft_panel.month_year.map(pd.Timestamp.toordinal)\n",
      "/tmp/ipykernel_3905719/1564898668.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  bef_deca['trend'] = intercept + slope * bef_deca.month_year.map(pd.Timestamp.toordinal)\n",
      "/tmp/ipykernel_3905719/1564898668.py:25: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  aft_deca['trend'] = intercept + slope * aft_deca.month_year.map(pd.Timestamp.toordinal)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import linregress\n",
    "\n",
    "bef_panel = info_qual_panel[info_qual_panel.month_year<'2022-11-01']\n",
    "aft_panel = info_qual_panel[info_qual_panel.month_year>='2022-11-01']\n",
    "# Calculate trend line using linear regression\n",
    "slope, intercept, r_value, p_value, std_err = linregress(bef_panel.month_year.map(pd.Timestamp.toordinal), bef_panel['weighted_average'])\n",
    "bef_panel['trend'] = intercept + slope * bef_panel.month_year.map(pd.Timestamp.toordinal)\n",
    "\n",
    "# Calculate trend line using linear regression\n",
    "slope, intercept, r_value, p_value, std_err = linregress(aft_panel.month_year.map(pd.Timestamp.toordinal), aft_panel['weighted_average'])\n",
    "aft_panel['trend'] = intercept + slope * aft_panel.month_year.map(pd.Timestamp.toordinal)\n",
    "\n",
    "\n",
    "\n",
    "bef_deca = info_qual_decahose[info_qual_decahose.month_year<'2022-11-01']\n",
    "aft_deca = info_qual_decahose[info_qual_decahose.month_year>='2022-11-01']\n",
    "# Calculate trend line using linear regression\n",
    "slope, intercept, r_value, p_value, std_err = linregress(bef_deca.month_year.map(pd.Timestamp.toordinal), bef_deca['weighted_average'])\n",
    "bef_deca['trend'] = intercept + slope * bef_deca.month_year.map(pd.Timestamp.toordinal)\n",
    "\n",
    "# Calculate trend line using linear regression\n",
    "slope, intercept, r_value, p_value, std_err = linregress(aft_deca.month_year.map(pd.Timestamp.toordinal), aft_deca['weighted_average'])\n",
    "aft_deca['trend'] = intercept + slope * aft_deca.month_year.map(pd.Timestamp.toordinal)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8b888841-8a7c-490d-a17d-055b85cfc29b",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'bef_panel' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[7], line 14\u001b[0m\n\u001b[1;32m     11\u001b[0m plt\u001b[38;5;241m.\u001b[39maxvline(x\u001b[38;5;241m=\u001b[39mcomplete_nov, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblack\u001b[39m\u001b[38;5;124m'\u001b[39m, ls\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     12\u001b[0m \u001b[38;5;66;03m#plt.text(x=complete_nov, y=68, s='Elon Musk buys Twitter', color='black', ha='right', va='bottom',fontsize=11, rotation=90)\u001b[39;00m\n\u001b[0;32m---> 14\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(\u001b[43mbef_panel\u001b[49m\u001b[38;5;241m.\u001b[39mmonth_year, bef_panel[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrend\u001b[39m\u001b[38;5;124m'\u001b[39m], color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124morange\u001b[39m\u001b[38;5;124m'\u001b[39m,ls\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m--\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     15\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(aft_panel\u001b[38;5;241m.\u001b[39mmonth_year, aft_panel[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrend\u001b[39m\u001b[38;5;124m'\u001b[39m], color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124morange\u001b[39m\u001b[38;5;124m'\u001b[39m,ls\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m--\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     16\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(bef_deca\u001b[38;5;241m.\u001b[39mmonth_year, bef_deca[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrend\u001b[39m\u001b[38;5;124m'\u001b[39m], color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblue\u001b[39m\u001b[38;5;124m'\u001b[39m, ls\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m--\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'bef_panel' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the result\n",
    "#info_qual_panel\n",
    "from datetime import date\n",
    "complete_nov = date(2022,10,27)\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10, 6))#plt.figure(figsize=(20, 6), dpi=300) \n",
    "plt.scatter(info_qual_decahose.month_year, info_qual_decahose.weighted_average, color='blue', label='Decahose')\n",
    "plt.scatter(info_qual_panel.month_year, info_qual_panel.weighted_average, color='orange', label='Panel')\n",
    "#plt.axvline(x=complete_nov, color='black', ls=\":\" ,label='Elon Musk buys Twitter')\n",
    "plt.axvline(x=complete_nov, color='black', ls=\":\")\n",
    "#plt.text(x=complete_nov, y=68, s='Elon Musk buys Twitter', color='black', ha='right', va='bottom',fontsize=11, rotation=90)\n",
    "\n",
    "plt.plot(bef_panel.month_year, bef_panel['trend'], color='orange',ls='--')\n",
    "plt.plot(aft_panel.month_year, aft_panel['trend'], color='orange',ls='--')\n",
    "plt.plot(bef_deca.month_year, bef_deca['trend'], color='blue', ls='--')\n",
    "plt.plot(aft_deca.month_year, aft_deca['trend'],  color='blue',ls='--')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.title('Information Quality Before and After Ownership Change on Twitter/X')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Information Quality')\n",
    "#plt.savefig('/home/ozturan/covid-conspiracy-narratives/src/musk/submission/figures/newsguard_information_quality.png',dpi=1200,bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "4c059864-36d9-4300-aead-533d1714b702",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Your time series data\n",
    "data = {'month_year': ['2022-01-01', '2022-02-01', '2022-03-01', '2022-04-01', \n",
    "                       '2022-05-01', '2022-06-01', '2022-07-01', '2022-08-01', \n",
    "                       '2022-09-01', '2022-10-01', '2022-11-01', '2022-12-01', \n",
    "                       '2023-01-01', '2023-02-01', '2023-03-01', '2023-04-01'],\n",
    "        'weighted_average': [84.630901, 84.632702, 85.686346, 85.636857, \n",
    "                             84.908175, 85.019544, 85.007498, 85.459850, \n",
    "                             85.680851, 85.609270, 84.848904, 83.876740, \n",
    "                             82.560827, 83.564816, 84.267286, 82.545730]}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "df['month_year'] = pd.to_datetime(df['month_year'])\n",
    "\n",
    "# Bootstrapping function for each time point\n",
    "def bootstrap_ci(data, n_bootstrap=1000, ci=95):\n",
    "    bootstrapped_means = []\n",
    "    \n",
    "    for i in range(n_bootstrap):\n",
    "        resample = np.random.choice(data, size=len(data), replace=True)\n",
    "        bootstrapped_means.append(np.mean(resample))\n",
    "    \n",
    "    lower_bound = np.percentile(bootstrapped_means, (100-ci)/2)\n",
    "    upper_bound = np.percentile(bootstrapped_means, 100 - (100-ci)/2)\n",
    "    \n",
    "    return lower_bound, upper_bound\n",
    "\n",
    "# Apply bootstrapping for each point in the time series\n",
    "lower_bounds = []\n",
    "upper_bounds = []\n",
    "\n",
    "for value in df['weighted_average']:\n",
    "    lower, upper = bootstrap_ci([value])\n",
    "    lower_bounds.append(lower)\n",
    "    upper_bounds.append(upper)\n",
    "\n",
    "# Calculate the error values for the error bars (upper - original and original - lower)\n",
    "errors = np.vstack((df['weighted_average'] - lower_bounds, upper_bounds - df['weighted_average']))\n",
    "\n",
    "# Plotting the original time series as a scatter plot with error bars\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Scatter plot with error bars\n",
    "plt.errorbar(df['month_year'], df['weighted_average'], yerr=errors, fmt='.', capsize=5, capthick=2, elinewidth=2, \n",
    "             label='Weighted Average', markersize=8, markerfacecolor='blue', markeredgewidth=1.5)\n",
    "\n",
    "# Add labels and legend\n",
    "plt.title('Scatter Plot with Bootstrapped 95% Confidence Intervals (Error Bars)')\n",
    "plt.xlabel('Month-Year')\n",
    "plt.ylabel('Weighted Average')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b2f19c63-7860-41a2-a907-93f5a0cbd6ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4013325/3622935283.py:26: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  info_qual_decahose['month_year'] = info_qual_decahose['month_year'].dt.to_timestamp()\n",
      "/tmp/ipykernel_4013325/3622935283.py:47: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  info_qual_panel ['month_year'] = info_qual_panel['month_year'].dt.to_timestamp()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "top =['youtube.com']\n",
    "# Step 2: Filter the DataFrame to exclude rows with these domains\n",
    "filtered_df = merged_df[~merged_df['expanded_domain'].isin(top)]\n",
    "filtered_df['pc1_weighted_count']=filtered_df['count'] *filtered_df['pc1']\n",
    "# Step 3: Convert the 'date' column to datetime format\n",
    "filtered_df['date'] = pd.to_datetime(filtered_df['date'])\n",
    "\n",
    "# Step 4: Group by month and year and calculate the monthly average of 'pc1_weighted_count'\n",
    "filtered_df['month_year'] = filtered_df['date'].dt.to_period('M')\n",
    "\n",
    "# Group by 'month_year' and calculate the sum of 'pc1_weighted_count'\n",
    "pc1_sum = filtered_df.groupby('month_year')['pc1_weighted_count'].sum().reset_index()\n",
    "\n",
    "# Group by 'month_year' and calculate the sum of 'count'\n",
    "count_sum = filtered_df.groupby('month_year')['count'].sum().reset_index()\n",
    "\n",
    "# Merge the two DataFrames based on 'month_year'\n",
    "merged_sums = pd.merge(pc1_sum, count_sum, on='month_year')\n",
    "# Perform the division of 'pc1_weighted_count' sum by 'count' sum\n",
    "result = merged_sums['pc1_weighted_count'] / merged_sums['count']\n",
    "# Add the result to the merged DataFrame\n",
    "merged_sums['weighted_average'] = result\n",
    "# Optionally, keep only relevant columns\n",
    "info_qual_decahose = merged_sums[['month_year', 'weighted_average']]\n",
    "# Convert PeriodDtype to Timestamp\n",
    "info_qual_decahose['month_year'] = info_qual_decahose['month_year'].dt.to_timestamp()\n",
    "info_qual_decahose= info_qual_decahose.iloc[1:-1]\n",
    "\n",
    "top6_panel = ['youtube.com','yt.vu']\n",
    "panel_merged_df['pc1_weighted_count']=panel_merged_df['count'] *panel_merged_df['pc1']\n",
    "# Group by 'month_year' and calculate the sum of 'pc1_weighted_count'\n",
    "filtered_df = panel_merged_df[~panel_merged_df['expanded_domain'].isin(top6_panel)]\n",
    "filtered_df['month_year'] = filtered_df['date'].dt.to_period('M')\n",
    "pc1_sum = filtered_df.groupby('month_year')['pc1_weighted_count'].sum().reset_index()\n",
    "\n",
    "# Group by 'month_year' and calculate the sum of 'count'\n",
    "count_sum = filtered_df.groupby('month_year')['count'].sum().reset_index()\n",
    "\n",
    "# Merge the two DataFrames based on 'month_year'\n",
    "merged_sums = pd.merge(pc1_sum, count_sum, on='month_year')\n",
    "\n",
    "# Perform the division of 'pc1_weighted_count' sum by 'count' sum\n",
    "merged_sums['weighted_average'] = merged_sums['pc1_weighted_count'] / merged_sums['count']\n",
    "\n",
    "# Optionally, keep only relevant columns\n",
    "info_qual_panel = merged_sums[['month_year', 'weighted_average']]\n",
    "info_qual_panel ['month_year'] = info_qual_panel['month_year'].dt.to_timestamp()\n",
    "# Convert PeriodDtype to Timestamp\n",
    "# Display the result\n",
    "#info_qual_panel\n",
    "from datetime import date\n",
    "complete_nov = date(2022,10,27)\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10, 6))#plt.figure(figsize=(20, 6), dpi=300) \n",
    "plt.scatter(info_qual_decahose.month_year, info_qual_decahose.weighted_average, color='blue', label='Decahose')\n",
    "plt.scatter(info_qual_panel.month_year, info_qual_panel.weighted_average, color='orange', label='Panel')\n",
    "#plt.axvline(x=complete_nov, color='black', ls=\":\" ,label='Elon Musk buys Twitter')\n",
    "plt.axvline(x=complete_nov, color='black', ls=\":\")\n",
    "#plt.text(x=complete_nov, y=68, s='Elon Musk buys Twitter', color='black', ha='right', va='bottom',fontsize=11, rotation=90)\n",
    "\n",
    "#plt.plot(bef_panel.month_year, 100*bef_panel['trend'], color='gray')\n",
    "#plt.plot(aft_panel.month_year, 100*aft_panel['trend'], color='gray')\n",
    "#plt.plot(bef_deca.month_year, 100*bef_deca['trend'], color='red')\n",
    "#plt.plot(aft_deca.month_year, 100*aft_deca['trend'],  color='red')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.title('Information Quality with Lin et al. (2022) Score')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Information Quality')\n",
    "#plt.savefig('/home/ozturan/covid-conspiracy-narratives/src/musk/submission/figures/information_quality_no_big_outlets.png',dpi=1200,bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8798527d-e5de-4286-be67-9a17fc560feb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "expanded_domain\n",
       "rawstory.com             136846\n",
       "theguardian.com          112033\n",
       "msn.com                  102144\n",
       "wsj.com                   82315\n",
       "news.yahoo.com            75738\n",
       "apnews.com                64496\n",
       "thehill.com               45982\n",
       "huffpost.com              45393\n",
       "conservativebrief.com     42945\n",
       "thedailybeast.com         36205\n",
       "dailykos.com              35869\n",
       "variety.com               34862\n",
       "axios.com                 32742\n",
       "thegatewaypundit.com      32141\n",
       "newsweek.com              31318\n",
       "usatoday.com              29543\n",
       "zerohedge.com             27239\n",
       "bbc.com                   26626\n",
       "msnbc.com                 22090\n",
       "palmerreport.com          22032\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "panel_merged_df.groupby('expanded_domain')['count'].sum().sort_values(ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5416c5c4-56a6-4dbc-adbf-2da08e8e6baa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ozturan/segregation/lib/python3.12/site-packages/scipy/stats/_axis_nan_policy.py:418: UserWarning: `kurtosistest` p-value may be inaccurate with fewer than 20 observations; only n=16 observations were given.\n",
      "  return hypotest_fun_in(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>info_qual</td>    <th>  R-squared:         </th> <td>   0.864</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.830</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   25.48</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 09 Oct 2024</td> <th>  Prob (F-statistic):</th> <td>1.72e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:59:22</td>     <th>  Log-Likelihood:    </th> <td> -7.8831</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    16</td>      <th>  AIC:               </th> <td>   23.77</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    12</td>      <th>  BIC:               </th> <td>   26.86</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>                <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>               <td>   84.2937</td> <td>    0.269</td> <td>  313.613</td> <td> 0.000</td> <td>   83.708</td> <td>   84.879</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>time</th>                    <td>    0.3974</td> <td>    0.050</td> <td>    7.894</td> <td> 0.000</td> <td>    0.288</td> <td>    0.507</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intervention</th>            <td>   -1.5574</td> <td>    0.455</td> <td>   -3.422</td> <td> 0.005</td> <td>   -2.549</td> <td>   -0.566</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>time_after_intervention</th> <td>   -0.7794</td> <td>    0.120</td> <td>   -6.476</td> <td> 0.000</td> <td>   -1.042</td> <td>   -0.517</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.809</td> <th>  Durbin-Watson:     </th> <td>   1.458</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.405</td> <th>  Jarque-Bera (JB):  </th> <td>   0.925</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.037</td> <th>  Prob(JB):          </th> <td>   0.630</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 1.825</td> <th>  Cond. No.          </th> <td>    36.9</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}            &    info\\_qual    & \\textbf{  R-squared:         } &     0.864   \\\\\n",
       "\\textbf{Model:}                    &       OLS        & \\textbf{  Adj. R-squared:    } &     0.830   \\\\\n",
       "\\textbf{Method:}                   &  Least Squares   & \\textbf{  F-statistic:       } &     25.48   \\\\\n",
       "\\textbf{Date:}                     & Wed, 09 Oct 2024 & \\textbf{  Prob (F-statistic):} &  1.72e-05   \\\\\n",
       "\\textbf{Time:}                     &     11:59:22     & \\textbf{  Log-Likelihood:    } &   -7.8831   \\\\\n",
       "\\textbf{No. Observations:}         &          16      & \\textbf{  AIC:               } &     23.77   \\\\\n",
       "\\textbf{Df Residuals:}             &          12      & \\textbf{  BIC:               } &     26.86   \\\\\n",
       "\\textbf{Df Model:}                 &           3      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}          &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}                 &      84.2937  &        0.269     &   313.613  &         0.000        &       83.708    &       84.879     \\\\\n",
       "\\textbf{time}                      &       0.3974  &        0.050     &     7.894  &         0.000        &        0.288    &        0.507     \\\\\n",
       "\\textbf{intervention}              &      -1.5574  &        0.455     &    -3.422  &         0.005        &       -2.549    &       -0.566     \\\\\n",
       "\\textbf{time\\_after\\_intervention} &      -0.7794  &        0.120     &    -6.476  &         0.000        &       -1.042    &       -0.517     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       &  1.809 & \\textbf{  Durbin-Watson:     } &    1.458  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.405 & \\textbf{  Jarque-Bera (JB):  } &    0.925  \\\\\n",
       "\\textbf{Skew:}          & -0.037 & \\textbf{  Prob(JB):          } &    0.630  \\\\\n",
       "\\textbf{Kurtosis:}      &  1.825 & \\textbf{  Cond. No.          } &     36.9  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              info_qual   R-squared:                       0.864\n",
       "Model:                            OLS   Adj. R-squared:                  0.830\n",
       "Method:                 Least Squares   F-statistic:                     25.48\n",
       "Date:                Wed, 09 Oct 2024   Prob (F-statistic):           1.72e-05\n",
       "Time:                        11:59:22   Log-Likelihood:                -7.8831\n",
       "No. Observations:                  16   AIC:                             23.77\n",
       "Df Residuals:                      12   BIC:                             26.86\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===========================================================================================\n",
       "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------------\n",
       "Intercept                  84.2937      0.269    313.613      0.000      83.708      84.879\n",
       "time                        0.3974      0.050      7.894      0.000       0.288       0.507\n",
       "intervention               -1.5574      0.455     -3.422      0.005      -2.549      -0.566\n",
       "time_after_intervention    -0.7794      0.120     -6.476      0.000      -1.042      -0.517\n",
       "==============================================================================\n",
       "Omnibus:                        1.809   Durbin-Watson:                   1.458\n",
       "Prob(Omnibus):                  0.405   Jarque-Bera (JB):                0.925\n",
       "Skew:                          -0.037   Prob(JB):                        0.630\n",
       "Kurtosis:                       1.825   Cond. No.                         36.9\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from statsmodels.formula.api import ols\n",
    "info_qual_panel['info_qual'] = info_qual_panel['weighted_average']*1\n",
    "intervention_date = pd.to_datetime('2022-10-27')\n",
    "# Create a time variable\n",
    "info_qual_panel['time'] = np.arange(len(info_qual_panel))\n",
    "# Create an intervention variable\n",
    "info_qual_panel['intervention'] = (info_qual_panel.month_year >= intervention_date).astype(int)\n",
    "# Create a time after intervention variable\n",
    "info_qual_panel['time_after_intervention'] = np.where(info_qual_panel.month_year >= intervention_date, info_qual_panel['time'] - info_qual_panel['time'][info_qual_panel['intervention'] == 1].min(), 0)\n",
    "# Perform ITS analysis using OLS regression\n",
    "model = ols('info_qual ~ time + intervention + time_after_intervention', data=info_qual_panel).fit()\n",
    "# Display the summary of the regression model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "43c7e374-f04a-4900-aa8f-255da03bdcea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ozturan/segregation/lib/python3.12/site-packages/scipy/stats/_axis_nan_policy.py:418: UserWarning: `kurtosistest` p-value may be inaccurate with fewer than 20 observations; only n=16 observations were given.\n",
      "  return hypotest_fun_in(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>info_qual</td>    <th>  R-squared:         </th> <td>   0.735</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.668</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   11.07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 09 Oct 2024</td> <th>  Prob (F-statistic):</th> <td>0.000902</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:59:51</td>     <th>  Log-Likelihood:    </th> <td> -11.971</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    16</td>      <th>  AIC:               </th> <td>   31.94</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    12</td>      <th>  BIC:               </th> <td>   35.03</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>                <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>               <td>   84.8663</td> <td>    0.347</td> <td>  244.552</td> <td> 0.000</td> <td>   84.110</td> <td>   85.622</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>time</th>                    <td>    0.0802</td> <td>    0.065</td> <td>    1.234</td> <td> 0.241</td> <td>   -0.061</td> <td>    0.222</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intervention</th>            <td>   -1.3904</td> <td>    0.588</td> <td>   -2.366</td> <td> 0.036</td> <td>   -2.671</td> <td>   -0.110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>time_after_intervention</th> <td>   -0.3471</td> <td>    0.155</td> <td>   -2.234</td> <td> 0.045</td> <td>   -0.686</td> <td>   -0.008</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.040</td> <th>  Durbin-Watson:     </th> <td>   2.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.595</td> <th>  Jarque-Bera (JB):  </th> <td>   0.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.016</td> <th>  Prob(JB):          </th> <td>   0.963</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.333</td> <th>  Cond. No.          </th> <td>    36.9</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}            &    info\\_qual    & \\textbf{  R-squared:         } &     0.735   \\\\\n",
       "\\textbf{Model:}                    &       OLS        & \\textbf{  Adj. R-squared:    } &     0.668   \\\\\n",
       "\\textbf{Method:}                   &  Least Squares   & \\textbf{  F-statistic:       } &     11.07   \\\\\n",
       "\\textbf{Date:}                     & Wed, 09 Oct 2024 & \\textbf{  Prob (F-statistic):} &  0.000902   \\\\\n",
       "\\textbf{Time:}                     &     11:59:51     & \\textbf{  Log-Likelihood:    } &   -11.971   \\\\\n",
       "\\textbf{No. Observations:}         &          16      & \\textbf{  AIC:               } &     31.94   \\\\\n",
       "\\textbf{Df Residuals:}             &          12      & \\textbf{  BIC:               } &     35.03   \\\\\n",
       "\\textbf{Df Model:}                 &           3      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}          &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}                 &      84.8663  &        0.347     &   244.552  &         0.000        &       84.110    &       85.622     \\\\\n",
       "\\textbf{time}                      &       0.0802  &        0.065     &     1.234  &         0.241        &       -0.061    &        0.222     \\\\\n",
       "\\textbf{intervention}              &      -1.3904  &        0.588     &    -2.366  &         0.036        &       -2.671    &       -0.110     \\\\\n",
       "\\textbf{time\\_after\\_intervention} &      -0.3471  &        0.155     &    -2.234  &         0.045        &       -0.686    &       -0.008     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       &  1.040 & \\textbf{  Durbin-Watson:     } &    2.005  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.595 & \\textbf{  Jarque-Bera (JB):  } &    0.074  \\\\\n",
       "\\textbf{Skew:}          & -0.016 & \\textbf{  Prob(JB):          } &    0.963  \\\\\n",
       "\\textbf{Kurtosis:}      &  3.333 & \\textbf{  Cond. No.          } &     36.9  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              info_qual   R-squared:                       0.735\n",
       "Model:                            OLS   Adj. R-squared:                  0.668\n",
       "Method:                 Least Squares   F-statistic:                     11.07\n",
       "Date:                Wed, 09 Oct 2024   Prob (F-statistic):           0.000902\n",
       "Time:                        11:59:51   Log-Likelihood:                -11.971\n",
       "No. Observations:                  16   AIC:                             31.94\n",
       "Df Residuals:                      12   BIC:                             35.03\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===========================================================================================\n",
       "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------------\n",
       "Intercept                  84.8663      0.347    244.552      0.000      84.110      85.622\n",
       "time                        0.0802      0.065      1.234      0.241      -0.061       0.222\n",
       "intervention               -1.3904      0.588     -2.366      0.036      -2.671      -0.110\n",
       "time_after_intervention    -0.3471      0.155     -2.234      0.045      -0.686      -0.008\n",
       "==============================================================================\n",
       "Omnibus:                        1.040   Durbin-Watson:                   2.005\n",
       "Prob(Omnibus):                  0.595   Jarque-Bera (JB):                0.074\n",
       "Skew:                          -0.016   Prob(JB):                        0.963\n",
       "Kurtosis:                       3.333   Cond. No.                         36.9\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_qual_decahose['info_qual'] = info_qual_decahose['weighted_average']*1\n",
    "# Create a time variable\n",
    "\n",
    "info_qual_decahose['time'] = np.arange(len(info_qual_decahose))\n",
    "# Create an intervention variable\n",
    "info_qual_decahose['intervention'] = (info_qual_decahose.month_year >= intervention_date).astype(int)\n",
    "\n",
    "# Create a time after intervention variable\n",
    "info_qual_decahose['time_after_intervention'] = np.where(info_qual_decahose.month_year >= intervention_date, info_qual_decahose['time'] - info_qual_decahose['time'][info_qual_decahose['intervention'] == 1].min(), 0)\n",
    "# Perform ITS analysis using OLS regression\n",
    "model = ols('info_qual ~ time + intervention + time_after_intervention', data=info_qual_decahose).fit()\n",
    "# Display the summary of the regression model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "34dbe073-dd81-4b2e-855b-7b63fb06c331",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'info_qual_decahose' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[75], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43minfo_qual_decahose\u001b[49m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'info_qual_decahose' is not defined"
     ]
    }
   ],
   "source": [
    "info_qual_decahose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2e1c0f6e-9b14-475e-8265-8c909a9b14f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ozturan/segregation/lib/python3.12/site-packages/scipy/stats/_axis_nan_policy.py:418: UserWarning: `kurtosistest` p-value may be inaccurate with fewer than 20 observations; only n=16 observations were given.\n",
      "  return hypotest_fun_in(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>info_qual</td>    <th>  R-squared:         </th> <td>   0.735</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.668</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   11.07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 09 Oct 2024</td> <th>  Prob (F-statistic):</th> <td>0.000902</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:59:51</td>     <th>  Log-Likelihood:    </th> <td> -11.971</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    16</td>      <th>  AIC:               </th> <td>   31.94</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    12</td>      <th>  BIC:               </th> <td>   35.03</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>                <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>               <td>   84.8663</td> <td>    0.347</td> <td>  244.552</td> <td> 0.000</td> <td>   84.110</td> <td>   85.622</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>time</th>                    <td>    0.0802</td> <td>    0.065</td> <td>    1.234</td> <td> 0.241</td> <td>   -0.061</td> <td>    0.222</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intervention</th>            <td>   -1.3904</td> <td>    0.588</td> <td>   -2.366</td> <td> 0.036</td> <td>   -2.671</td> <td>   -0.110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>time_after_intervention</th> <td>   -0.3471</td> <td>    0.155</td> <td>   -2.234</td> <td> 0.045</td> <td>   -0.686</td> <td>   -0.008</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.040</td> <th>  Durbin-Watson:     </th> <td>   2.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.595</td> <th>  Jarque-Bera (JB):  </th> <td>   0.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.016</td> <th>  Prob(JB):          </th> <td>   0.963</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.333</td> <th>  Cond. No.          </th> <td>    36.9</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}            &    info\\_qual    & \\textbf{  R-squared:         } &     0.735   \\\\\n",
       "\\textbf{Model:}                    &       OLS        & \\textbf{  Adj. R-squared:    } &     0.668   \\\\\n",
       "\\textbf{Method:}                   &  Least Squares   & \\textbf{  F-statistic:       } &     11.07   \\\\\n",
       "\\textbf{Date:}                     & Wed, 09 Oct 2024 & \\textbf{  Prob (F-statistic):} &  0.000902   \\\\\n",
       "\\textbf{Time:}                     &     11:59:51     & \\textbf{  Log-Likelihood:    } &   -11.971   \\\\\n",
       "\\textbf{No. Observations:}         &          16      & \\textbf{  AIC:               } &     31.94   \\\\\n",
       "\\textbf{Df Residuals:}             &          12      & \\textbf{  BIC:               } &     35.03   \\\\\n",
       "\\textbf{Df Model:}                 &           3      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}          &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}                 &      84.8663  &        0.347     &   244.552  &         0.000        &       84.110    &       85.622     \\\\\n",
       "\\textbf{time}                      &       0.0802  &        0.065     &     1.234  &         0.241        &       -0.061    &        0.222     \\\\\n",
       "\\textbf{intervention}              &      -1.3904  &        0.588     &    -2.366  &         0.036        &       -2.671    &       -0.110     \\\\\n",
       "\\textbf{time\\_after\\_intervention} &      -0.3471  &        0.155     &    -2.234  &         0.045        &       -0.686    &       -0.008     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       &  1.040 & \\textbf{  Durbin-Watson:     } &    2.005  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.595 & \\textbf{  Jarque-Bera (JB):  } &    0.074  \\\\\n",
       "\\textbf{Skew:}          & -0.016 & \\textbf{  Prob(JB):          } &    0.963  \\\\\n",
       "\\textbf{Kurtosis:}      &  3.333 & \\textbf{  Cond. No.          } &     36.9  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              info_qual   R-squared:                       0.735\n",
       "Model:                            OLS   Adj. R-squared:                  0.668\n",
       "Method:                 Least Squares   F-statistic:                     11.07\n",
       "Date:                Wed, 09 Oct 2024   Prob (F-statistic):           0.000902\n",
       "Time:                        11:59:51   Log-Likelihood:                -11.971\n",
       "No. Observations:                  16   AIC:                             31.94\n",
       "Df Residuals:                      12   BIC:                             35.03\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===========================================================================================\n",
       "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------------\n",
       "Intercept                  84.8663      0.347    244.552      0.000      84.110      85.622\n",
       "time                        0.0802      0.065      1.234      0.241      -0.061       0.222\n",
       "intervention               -1.3904      0.588     -2.366      0.036      -2.671      -0.110\n",
       "time_after_intervention    -0.3471      0.155     -2.234      0.045      -0.686      -0.008\n",
       "==============================================================================\n",
       "Omnibus:                        1.040   Durbin-Watson:                   2.005\n",
       "Prob(Omnibus):                  0.595   Jarque-Bera (JB):                0.074\n",
       "Skew:                          -0.016   Prob(JB):                        0.963\n",
       "Kurtosis:                       3.333   Cond. No.                         36.9\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_qual_decahose['info_qual'] = info_qual_decahose['weighted_average']*1\n",
    "# Create a time variable\n",
    "\n",
    "info_qual_decahose['time'] = np.arange(len(info_qual_decahose))\n",
    "# Create an intervention variable\n",
    "info_qual_decahose['intervention'] = (info_qual_decahose.month_year >= intervention_date).astype(int)\n",
    "\n",
    "# Create a time after intervention variable\n",
    "info_qual_decahose['time_after_intervention'] = np.where(info_qual_decahose.month_year >= intervention_date, info_qual_decahose['time'] - info_qual_decahose['time'][info_qual_decahose['intervention'] == 1].min(), 0)\n",
    "# Perform ITS analysis using OLS regression\n",
    "model = ols('info_qual ~ time + intervention + time_after_intervention', data=info_qual_decahose).fit()\n",
    "# Display the summary of the regression model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f70069c8-99ba-47d7-9d6d-24f52dcc0151",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "59fe05ca-1974-4fef-8ba9-69d6f7d400f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ozturan/segregation/lib/python3.12/site-packages/scipy/stats/_axis_nan_policy.py:418: UserWarning: `kurtosistest` p-value may be inaccurate with fewer than 20 observations; only n=16 observations were given.\n",
      "  return hypotest_fun_in(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>info_qual</td>    <th>  R-squared:         </th> <td>   0.735</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.668</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   11.07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 09 Oct 2024</td> <th>  Prob (F-statistic):</th> <td>0.000902</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:59:51</td>     <th>  Log-Likelihood:    </th> <td> -11.971</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    16</td>      <th>  AIC:               </th> <td>   31.94</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    12</td>      <th>  BIC:               </th> <td>   35.03</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>                <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>               <td>   84.8663</td> <td>    0.347</td> <td>  244.552</td> <td> 0.000</td> <td>   84.110</td> <td>   85.622</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>time</th>                    <td>    0.0802</td> <td>    0.065</td> <td>    1.234</td> <td> 0.241</td> <td>   -0.061</td> <td>    0.222</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intervention</th>            <td>   -1.3904</td> <td>    0.588</td> <td>   -2.366</td> <td> 0.036</td> <td>   -2.671</td> <td>   -0.110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>time_after_intervention</th> <td>   -0.3471</td> <td>    0.155</td> <td>   -2.234</td> <td> 0.045</td> <td>   -0.686</td> <td>   -0.008</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.040</td> <th>  Durbin-Watson:     </th> <td>   2.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.595</td> <th>  Jarque-Bera (JB):  </th> <td>   0.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.016</td> <th>  Prob(JB):          </th> <td>   0.963</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.333</td> <th>  Cond. No.          </th> <td>    36.9</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}            &    info\\_qual    & \\textbf{  R-squared:         } &     0.735   \\\\\n",
       "\\textbf{Model:}                    &       OLS        & \\textbf{  Adj. R-squared:    } &     0.668   \\\\\n",
       "\\textbf{Method:}                   &  Least Squares   & \\textbf{  F-statistic:       } &     11.07   \\\\\n",
       "\\textbf{Date:}                     & Wed, 09 Oct 2024 & \\textbf{  Prob (F-statistic):} &  0.000902   \\\\\n",
       "\\textbf{Time:}                     &     11:59:51     & \\textbf{  Log-Likelihood:    } &   -11.971   \\\\\n",
       "\\textbf{No. Observations:}         &          16      & \\textbf{  AIC:               } &     31.94   \\\\\n",
       "\\textbf{Df Residuals:}             &          12      & \\textbf{  BIC:               } &     35.03   \\\\\n",
       "\\textbf{Df Model:}                 &           3      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}          &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}                 &      84.8663  &        0.347     &   244.552  &         0.000        &       84.110    &       85.622     \\\\\n",
       "\\textbf{time}                      &       0.0802  &        0.065     &     1.234  &         0.241        &       -0.061    &        0.222     \\\\\n",
       "\\textbf{intervention}              &      -1.3904  &        0.588     &    -2.366  &         0.036        &       -2.671    &       -0.110     \\\\\n",
       "\\textbf{time\\_after\\_intervention} &      -0.3471  &        0.155     &    -2.234  &         0.045        &       -0.686    &       -0.008     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       &  1.040 & \\textbf{  Durbin-Watson:     } &    2.005  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.595 & \\textbf{  Jarque-Bera (JB):  } &    0.074  \\\\\n",
       "\\textbf{Skew:}          & -0.016 & \\textbf{  Prob(JB):          } &    0.963  \\\\\n",
       "\\textbf{Kurtosis:}      &  3.333 & \\textbf{  Cond. No.          } &     36.9  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              info_qual   R-squared:                       0.735\n",
       "Model:                            OLS   Adj. R-squared:                  0.668\n",
       "Method:                 Least Squares   F-statistic:                     11.07\n",
       "Date:                Wed, 09 Oct 2024   Prob (F-statistic):           0.000902\n",
       "Time:                        11:59:51   Log-Likelihood:                -11.971\n",
       "No. Observations:                  16   AIC:                             31.94\n",
       "Df Residuals:                      12   BIC:                             35.03\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===========================================================================================\n",
       "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------------\n",
       "Intercept                  84.8663      0.347    244.552      0.000      84.110      85.622\n",
       "time                        0.0802      0.065      1.234      0.241      -0.061       0.222\n",
       "intervention               -1.3904      0.588     -2.366      0.036      -2.671      -0.110\n",
       "time_after_intervention    -0.3471      0.155     -2.234      0.045      -0.686      -0.008\n",
       "==============================================================================\n",
       "Omnibus:                        1.040   Durbin-Watson:                   2.005\n",
       "Prob(Omnibus):                  0.595   Jarque-Bera (JB):                0.074\n",
       "Skew:                          -0.016   Prob(JB):                        0.963\n",
       "Kurtosis:                       3.333   Cond. No.                         36.9\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_qual_decahose['info_qual'] = info_qual_decahose['weighted_average']*1\n",
    "# Create a time variable\n",
    "\n",
    "info_qual_decahose['time'] = np.arange(len(info_qual_decahose))\n",
    "# Create an intervention variable\n",
    "info_qual_decahose['intervention'] = (info_qual_decahose.month_year >= intervention_date).astype(int)\n",
    "\n",
    "# Create a time after intervention variable\n",
    "info_qual_decahose['time_after_intervention'] = np.where(info_qual_decahose.month_year >= intervention_date, info_qual_decahose['time'] - info_qual_decahose['time'][info_qual_decahose['intervention'] == 1].min(), 0)\n",
    "# Perform ITS analysis using OLS regression\n",
    "model = ols('info_qual ~ time + intervention + time_after_intervention', data=info_qual_decahose).fit()\n",
    "# Display the summary of the regression model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "10ffb709-aaa8-4e7c-9880-3805cfcd6f03",
   "metadata": {},
   "source": [
    "info_qual_decahose[six\n",
    "'info_qual'] = info_qual_decahose['weighted_average']*1\n",
    "# Create a time variable\n",
    "\n",
    "info_qual_decahose['time'] = np.arange(len(info_qual_decahose))\n",
    "# Create an intervention variable\n",
    "info_qual_decahose['intervention'] = (info_qual_decahose.month_year >= intervention_date).astype(int)\n",
    "\n",
    "# Create a time after intervention variable\n",
    "info_qual_decahose['time_after_intervention'] = np.where(info_qual_decahose.month_year >= intervention_date, info_qual_decahose['time'] - info_qual_decahose['time'][info_qual_decahose['intervention'] == 1].min(), 0)\n",
    "# Perform ITS analysis using OLS regression\n",
    "model = ols('info_qual ~ time + intervention + time_after_intervention', data=info_qual_decahose).fit()\n",
    "# Display the summary of the regression model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f76648d2-d078-41a9-a455-80d792360cd8",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'info_qual_decahose' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[74], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Step 1: Plot the ACF for the time series data\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m sm\u001b[38;5;241m.\u001b[39mgraphics\u001b[38;5;241m.\u001b[39mtsa\u001b[38;5;241m.\u001b[39mplot_acf(\u001b[43minfo_qual_decahose\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minfo_qual\u001b[39m\u001b[38;5;124m'\u001b[39m], lags\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m30\u001b[39m)\n\u001b[1;32m      6\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAutocorrelation of Info Quality Time Series\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      7\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "\u001b[0;31mNameError\u001b[0m: name 'info_qual_decahose' is not defined"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Step 1: Plot the ACF for the time series data\n",
    "sm.graphics.tsa.plot_acf(info_qual_decahose['info_qual'], lags=30)\n",
    "plt.title(\"Autocorrelation of Info Quality Time Series\")\n",
    "plt.show()\n",
    "\n",
    "# Step 2: Perform Durbin-Watson Test for autocorrelation in residuals of the ITS model\n",
    "from statsmodels.stats.stattools import durbin_watson\n",
    "\n",
    "# Calculate the Durbin-Watson statistic\n",
    "dw_stat = durbin_watson(model.resid)\n",
    "print(f'Durbin-Watson statistic: {dw_stat}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e5950da-8647-487d-8f93-e29fc69a417d",
   "metadata": {},
   "source": [
    "# Market Share"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b41eeed5-a957-4cf1-bd49-6525d878ebed",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1065075/2606191835.py:12: DtypeWarning: Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv('/home/ozturan/covid-conspiracy-narratives/src/musk/submission/figures/deca_mis_conc_with_shortener3.tsv', sep= '\\t')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "newsguard = pd.read_csv('/net/lazer/lab-lazer/shared_projects/datasets/newsguard/metadata/2023-05.csv')\n",
    "newsguard.rename(columns={\"Domain\": \"expanded_domain\"}, inplace=True)\n",
    "newsguard = newsguard[~newsguard['Rating'].isin(['P', 'S'])]\n",
    "newsguard = newsguard.drop_duplicates(subset=['expanded_domain'], keep='first').reset_index(drop=True)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "df = pd.read_csv('/home/ozturan/covid-conspiracy-narratives/src/musk/submission/figures/deca_mis_conc_with_shortener3.tsv', sep= '\\t')\n",
    "domain_ratings = pd.read_csv('/home/ozturan/covid-conspiracy-narratives/src/domain_ratings_with_shorteners.csv')\n",
    "domain_ratings  = domain_ratings.drop(columns=['Unnamed: 0'])#df = df.drop(columns=['Unnamed: 0'])\n",
    "df_deduplicated  = df.drop_duplicates(subset=['date','expanded_domain']).reset_index(drop=True)\n",
    "merged_df = pd.merge(df, domain_ratings, on='expanded_domain', how='inner')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "merged_df = pd.merge(merged_df, newsguard, on='expanded_domain', how='inner')\n",
    "\n",
    "merged_df['count'] = merged_df['count'].astype(int) #df['count'] = df['count'].astype(int)\n",
    "merged_df = merged_df.drop_duplicates(subset=['date','expanded_domain']).reset_index(drop=True)\n",
    "# Select specific columns\n",
    "merged_df = merged_df[['date', 'expanded_domain', 'count', 'pc1','Score']]\n",
    "\n",
    "top =['youtube.com']\n",
    "# Step 2: Filter the DataFrame to exclude rows with these domains\n",
    "merged_df = merged_df[~merged_df['expanded_domain'].isin(top)]\n",
    "\n",
    "\n",
    "number_of_share_count = merged_df ['count'].sum()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a932711f-b174-434b-9f38-57f72d4fe836",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "panel = pd.read_csv('/home/ozturan/covid-conspiracy-narratives/src/panel_daily_urls_withbackfilled_andkenny.csv')\n",
    "panel = panel.drop('Unnamed: 0', axis=1)\n",
    "panel_deduplicated  = panel.drop_duplicates(subset=['id']).reset_index(drop=True)\n",
    "panel_merged_df = panel_deduplicated[(panel_deduplicated['date'] >= '2022-01-01') & (panel_deduplicated['date'] < '2023-05-01') ].reset_index(drop=True)\n",
    "\n",
    "panel_merged_df=panel_merged_df.rename(columns={'domain': 'expanded_domain'})\n",
    "\n",
    "# Ensure 'date' is datetime type\n",
    "panel_merged_df['date'] = pd.to_datetime(panel_merged_df['date'])\n",
    "panel_merged_df = panel_merged_df.groupby(['date', 'expanded_domain', 'pc1']).size().reset_index(name='count')\n",
    "panel_merged_df['count'] = panel_merged_df['count'].astype(int)\n",
    "\n",
    "\n",
    "panel_merged_df = pd.merge(panel_merged_df, newsguard, on='expanded_domain', how='inner')\n",
    "\n",
    "top =['youtube.com','yt.vu']\n",
    "p_merged_df = panel_merged_df[~panel_merged_df['expanded_domain'].isin(top)]\n",
    "number_of_share_count = p_merged_df['count'].sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "85ee4ca2-4590-4abb-b8d9-d710576a6070",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decahose: {'75-100': np.float64(0.7671360647784973), '60-74': np.float64(0.11706873472131049), '40-59': np.float64(0.031087811230916515), '0-39': np.float64(0.06149907808746099)}\n",
      "Twitter Panel: {'75-100': np.float64(0.7770318487797051), '60-74': np.float64(0.08445641540429677), '40-59': np.float64(0.03674529353383587), '0-39': np.float64(0.07259409027725519)}\n"
     ]
    }
   ],
   "source": [
    "# Define custom intervals\n",
    "intervals = [(75, 100), (60, 74), (40, 59), (0, 39)]\n",
    "\n",
    "# Initialize the decahose and twitter_panel dictionaries\n",
    "decahose = {}\n",
    "twitter_panel = {}\n",
    "number_of_share_count_panel = p_merged_df['count'].sum()\n",
    "number_of_share_count_deca = merged_df ['count'].sum()\n",
    "# Loop through the custom intervals\n",
    "for lower_bound, upper_bound in intervals:\n",
    "    # Filter data for decahose within the custom intervals\n",
    "    filtered_df = merged_df[(merged_df['Score'] > lower_bound) & (merged_df['Score'] <= upper_bound)]\n",
    "    quintile_count = filtered_df['count'].sum()\n",
    "    decahose[f'{lower_bound}-{upper_bound}'] = quintile_count / number_of_share_count_deca\n",
    "    \n",
    "    # Filter data for twitter_panel within the custom intervals\n",
    "    panel_filtered_df = p_merged_df[(p_merged_df['Score'] > lower_bound) & (p_merged_df['Score'] <= upper_bound)]\n",
    "    quintile_count = panel_filtered_df['count'].sum()\n",
    "    twitter_panel[f'{lower_bound}-{upper_bound}'] = quintile_count / number_of_share_count_panel\n",
    "\n",
    "# Print results for verification\n",
    "print(\"Decahose:\", decahose)\n",
    "print(\"Twitter Panel:\", twitter_panel)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1b07366f-ca20-4008-9cd1-7ed32d73f533",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Custom intervals and corresponding labels\n",
    "labels = ['[0-39]', '[40-59]', '[60-74]', '[75-100]']\n",
    "\n",
    "# Extract values for Twitter Panel and Decahose using the custom intervals\n",
    "twitter_panel_values = [twitter_panel['0-39'], twitter_panel['40-59'], twitter_panel['60-74'], twitter_panel['75-100']]\n",
    "decahose_values = [decahose['0-39'], decahose['40-59'], decahose['60-74'], decahose['75-100']]\n",
    "\n",
    "# Plotting\n",
    "x = np.arange(len(labels))  # label locations\n",
    "width = 0.35  # width of the bars\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "# Bar plots for Twitter Panel and Decahose\n",
    "rects1 = ax.bar(x - width/2, twitter_panel_values, width, label='Twitter Panel', color='orange')\n",
    "rects2 = ax.bar(x + width/2, decahose_values, width, label='Decahose', color='blue')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_xlabel('Information Quality Score Range')\n",
    "ax.set_ylabel('Market Share')\n",
    "ax.set_title('Market Share by NewsGuard Reliability Score for Twitter Panel and Decahose')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(labels)\n",
    "ax.legend()\n",
    "\n",
    "# Function to label the bars with percentage values\n",
    "def autolabel(rects):\n",
    "    \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.annotate(f'{height:.2%}',  # Format as percentage\n",
    "                    xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "                    xytext=(0, 3),  # 3 points vertical offset\n",
    "                    textcoords=\"offset points\",\n",
    "                    ha='center', va='bottom')\n",
    "\n",
    "# Label the bars\n",
    "autolabel(rects1)\n",
    "autolabel(rects2)\n",
    "\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "#plt.savefig('/home/ozturan/covid-conspiracy-narratives/src/musk/submission/figures/newsguard_4bins_marketshare_change.png', dpi=1200, bbox_inches='tight')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3315685-7c21-4e8d-8995-efa949377d37",
   "metadata": {},
   "source": [
    "# Change Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b0edd41c-7856-458c-ade2-2bb0139f5ec9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[np.float64(17.541242380072518), np.float64(-1.9501306982965025)]\n",
      "[np.float64(-0.008961837282903415), np.float64(-16.312302796828654)]\n"
     ]
    }
   ],
   "source": [
    "####change in the relative importance of the each quintiles #####\n",
    "\n",
    "x_deca =[]\n",
    "y_deca= []\n",
    "y_deca_vol = []\n",
    "\n",
    "for i in range(0,120,60):\n",
    "    thrshold = i\n",
    "    #print(thrshold)\n",
    "    filtered_df = merged_df[(merged_df['Score'] > thrshold) & (merged_df['Score'] <= thrshold+60)]\n",
    "    daily_sum = filtered_df.groupby('date')['count'].sum()\n",
    "    daily_sum_all = merged_df.groupby('date')['count'].sum()\n",
    "    daily_ratio =daily_sum/daily_sum_all\n",
    "    daily_ratio\n",
    "    change = (daily_sum[299:485].mean()- daily_sum[:299].mean())/daily_sum[:299].mean()\n",
    "    change_r = (daily_ratio[299:485].mean()- daily_ratio[:299].mean())/daily_ratio[:299].mean()\n",
    "    x_deca.append(thrshold)\n",
    "    y_deca.append(change_r*100)\n",
    "    y_deca_vol.append(change*100)\n",
    "print(y_deca)\n",
    "print(y_deca_vol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b5824262-8b06-4a55-8161-b3a95355c33f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_vol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7c6525bf-59c7-4947-bca0-c145d733e8dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[np.float64(6.351463703921654), np.float64(-0.9480162670115521)]\n",
      "[np.float64(-23.01223635178439), np.float64(-26.856229670357042)]\n"
     ]
    }
   ],
   "source": [
    "x =[]\n",
    "y= []\n",
    "count_url_bef=[]\n",
    "count_url_aft = []\n",
    "y_vol=[]\n",
    "for i in range(0,120,60):\n",
    "    thrshold = i\n",
    "    #print(thrshold)\n",
    "    filtered_df = p_merged_df[(p_merged_df['Score'] > thrshold) & (p_merged_df['Score'] <= thrshold+60)]\n",
    "    #panel_merged_df[~panel_merged_df['expanded_domain'].isin(apple)]\n",
    "    daily_sum = filtered_df.groupby('date')['count'].sum()\n",
    "    daily_sum_all = p_merged_df.groupby('date')['count'].sum()\n",
    "    daily_ratio =daily_sum/daily_sum_all\n",
    "    daily_ratio\n",
    "    \n",
    "    bef_mean = daily_sum[:299].mean()\n",
    "    count_url_bef.append(bef_mean)\n",
    "    \n",
    "    aft_mean = daily_sum[299:485].mean()\n",
    "    count_url_aft.append(aft_mean)\n",
    "    \n",
    "    change = (daily_sum[299:485].mean()- daily_sum[:299].mean())/daily_sum[:299].mean()\n",
    "    change_r = (daily_ratio[299:485].mean()- daily_ratio[:299].mean())/daily_ratio[:299].mean()\n",
    "    \n",
    "    x.append(thrshold)\n",
    "    y_vol.append(change*100)\n",
    "    y.append(change_r*100)\n",
    "print(y)\n",
    "print(y_vol)\n",
    "#y_vol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "ff95b5aa-40e0-4a70-b0a9-8d5d0322fb0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[np.float64(0.015728876679264914),\n",
       " np.float64(39.90951951933271),\n",
       " np.float64(27.944326960482936),\n",
       " np.float64(6.890363579473541),\n",
       " np.float64(-4.406023489395666)]"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_deca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "8713c771-36f6-482c-855f-61acee4fefd6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Convert date columns to datetime (if not already)\n",
    "p_merged_df['date'] = pd.to_datetime(p_merged_df['date'])\n",
    "merged_df['date'] = pd.to_datetime(merged_df['date'])\n",
    "\n",
    "# Define quintile thresholds\n",
    "quintile_thresholds = [0, 60, 120]\n",
    "\n",
    "# Number of bootstrap samples\n",
    "n_bootstrap = 10\n",
    "\n",
    "# Initialize lists for storing bootstrapped results\n",
    "y_panel_bootstrap = []\n",
    "y_panel_vol_bootstrap = []\n",
    "y_deca_bootstrap = []\n",
    "y_deca_vol_bootstrap = []\n",
    "\n",
    "# Function for bootstrapping with confidence intervals\n",
    "def bootstrap_samples(data, n_bootstrap=n_bootstrap):\n",
    "    return [np.random.choice(data, size=len(data), replace=True) for _ in range(n_bootstrap)]\n",
    "\n",
    "# Function for bootstrapping\n",
    "def bootstrap_ci(data, n_bootstrap=n_bootstrap, ci=95):\n",
    "    if len(data) == 0:\n",
    "        return None  # Return None if data is empty\n",
    "    boot_means = [np.mean(sample) for sample in bootstrap_samples(data, n_bootstrap)]\n",
    "    lower_bound = np.percentile(boot_means, (100 - ci) / 2)\n",
    "    upper_bound = np.percentile(boot_means, 100 - (100 - ci) / 2)\n",
    "    return boot_means, lower_bound, upper_bound\n",
    "\n",
    "# Bootstrapping for **Panel Data** (p_merged_df)\n",
    "for thrshold in quintile_thresholds:\n",
    "    filtered_df = p_merged_df[(p_merged_df['Score'] > thrshold) & (p_merged_df['Score'] <= thrshold + 60)]\n",
    "    \n",
    "    daily_sum = filtered_df.groupby('date')['count'].sum()\n",
    "    daily_sum_all = p_merged_df.groupby('date')['count'].sum()\n",
    "    daily_ratio = daily_sum / daily_sum_all\n",
    "\n",
    "    # Skip if not enough data\n",
    "    if len(daily_sum) < 299:\n",
    "        continue\n",
    "\n",
    "    change_bootstrap = []\n",
    "    change_r_bootstrap = []\n",
    "\n",
    "    for _ in range(n_bootstrap):\n",
    "        sampled_indices_bef = np.random.choice(daily_sum.index[:299], size=299, replace=True)\n",
    "        sampled_indices_aft = np.random.choice(daily_sum.index[299:485], size=(485 - 299), replace=True)\n",
    "\n",
    "        if len(sampled_indices_bef) == 0 or len(sampled_indices_aft) == 0:\n",
    "            continue  # Skip empty cases\n",
    "\n",
    "        sampled_daily_sum_bef = daily_sum.loc[sampled_indices_bef]\n",
    "        sampled_daily_sum_aft = daily_sum.loc[sampled_indices_aft]\n",
    "\n",
    "        sampled_daily_ratio_bef = daily_ratio.loc[sampled_indices_bef]\n",
    "        sampled_daily_ratio_aft = daily_ratio.loc[sampled_indices_aft]\n",
    "\n",
    "        bef_mean = sampled_daily_sum_bef.mean()\n",
    "        aft_mean = sampled_daily_sum_aft.mean()\n",
    "        change = (aft_mean - bef_mean) / bef_mean if bef_mean != 0 else np.nan\n",
    "\n",
    "        bef_ratio_mean = sampled_daily_ratio_bef.mean()\n",
    "        aft_ratio_mean = sampled_daily_ratio_aft.mean()\n",
    "        change_r = (aft_ratio_mean - bef_ratio_mean) / bef_ratio_mean if bef_ratio_mean != 0 else np.nan\n",
    "\n",
    "        change_bootstrap.append(change * 100)\n",
    "        change_r_bootstrap.append(change_r * 100)\n",
    "\n",
    "    if change_bootstrap and change_r_bootstrap:\n",
    "        y_panel_bootstrap.append(bootstrap_ci(change_r_bootstrap))\n",
    "        y_panel_vol_bootstrap.append(bootstrap_ci(change_bootstrap))\n",
    "\n",
    "# Bootstrapping for **Decahose Data** (merged_df)\n",
    "for thrshold in quintile_thresholds:\n",
    "    filtered_df = merged_df[(merged_df['Score'] > thrshold) & (merged_df['Score'] <= thrshold + 60)]\n",
    "    \n",
    "    daily_sum = filtered_df.groupby('date')['count'].sum()\n",
    "    daily_sum_all = merged_df.groupby('date')['count'].sum()\n",
    "    daily_ratio = daily_sum / daily_sum_all\n",
    "\n",
    "    # Skip if not enough data\n",
    "    if len(daily_sum) < 299:\n",
    "        continue\n",
    "\n",
    "    change_bootstrap = []\n",
    "    change_r_bootstrap = []\n",
    "\n",
    "    for _ in range(n_bootstrap):\n",
    "        sampled_indices_bef = np.random.choice(daily_sum.index[:299], size=299, replace=True)\n",
    "        sampled_indices_aft = np.random.choice(daily_sum.index[299:485], size=(485 - 299), replace=True)\n",
    "\n",
    "        if len(sampled_indices_bef) == 0 or len(sampled_indices_aft) == 0:\n",
    "            continue\n",
    "\n",
    "        sampled_daily_sum_bef = daily_sum.loc[sampled_indices_bef]\n",
    "        sampled_daily_sum_aft = daily_sum.loc[sampled_indices_aft]\n",
    "\n",
    "        sampled_daily_ratio_bef = daily_ratio.loc[sampled_indices_bef]\n",
    "        sampled_daily_ratio_aft = daily_ratio.loc[sampled_indices_aft]\n",
    "\n",
    "        bef_mean = sampled_daily_sum_bef.mean()\n",
    "        aft_mean = sampled_daily_sum_aft.mean()\n",
    "        change = (aft_mean - bef_mean) / bef_mean if bef_mean != 0 else np.nan\n",
    "\n",
    "        bef_ratio_mean = sampled_daily_ratio_bef.mean()\n",
    "        aft_ratio_mean = sampled_daily_ratio_aft.mean()\n",
    "        change_r = (aft_ratio_mean - bef_ratio_mean) / bef_ratio_mean if bef_ratio_mean != 0 else np.nan\n",
    "\n",
    "        change_bootstrap.append(change * 100)\n",
    "        change_r_bootstrap.append(change_r * 100)\n",
    "\n",
    "    if change_bootstrap and change_r_bootstrap:\n",
    "        y_deca_bootstrap.append(bootstrap_ci(change_r_bootstrap))\n",
    "        y_deca_vol_bootstrap.append(bootstrap_ci(change_bootstrap))\n",
    "\n",
    "# Extract means and confidence intervals\n",
    "def extract_ci(bootstrap_results):\n",
    "    return (\n",
    "        [b[0] for b in bootstrap_results if b is not None],  # Means\n",
    "        [b[1] for b in bootstrap_results if b is not None],  # Lower CI\n",
    "        [b[2] for b in bootstrap_results if b is not None]   # Upper CI\n",
    "    )\n",
    "\n",
    "# Extract values for both datasets\n",
    "y_panel_means, _, _ = extract_ci(y_panel_bootstrap)\n",
    "y_panel_vol_means, _, _ = extract_ci(y_panel_vol_bootstrap)\n",
    "\n",
    "y_deca_means, _, _ = extract_ci(y_deca_bootstrap)\n",
    "y_deca_vol_means, _, _ = extract_ci(y_deca_vol_bootstrap)\n",
    "\n",
    "# Categories for plotting\n",
    "quintile_labels = [\"0-60\", \"60-120\", \"120-180\"]\n",
    "colors_panel = ['darkorange', 'orange', 'gold']\n",
    "colors_decahose = ['darkblue', 'mediumblue', 'royalblue']\n",
    "\n",
    "# Function to create **boxplots** with bootstrapped data\n",
    "def create_boxplot(ax, title, data, colors):\n",
    "    if len(data) == 0:\n",
    "        ax.set_title(title + \" (No Data)\", fontsize=14)\n",
    "        return\n",
    "    \n",
    "    box = ax.boxplot(data, patch_artist=True, medianprops=dict(color='black'), widths=0.8, notch=True, showfliers=False)\n",
    "\n",
    "    ax.set_title(title, fontsize=14)\n",
    "    ax.set_ylabel('Percentage Change', fontsize=12)\n",
    "    ax.set_xlabel('Quintile Ranges', fontsize=12)\n",
    "    ax.set_xticks(range(1, len(data) + 1))\n",
    "    ax.set_xticklabels(quintile_labels[:len(data)], rotation=0, ha='right', fontsize=11)\n",
    "    ax.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "    ax.axhline(0, color='black', linewidth=2)\n",
    "\n",
    "    for patch, color in zip(box['boxes'], colors):\n",
    "        patch.set_facecolor(color)\n",
    "\n",
    "# Create boxplots\n",
    "fig, axs = plt.subplots(2, 2, figsize=(14, 16), sharey=True)\n",
    "\n",
    "\n",
    "create_boxplot(axs[0, 0], 'Decahose: Importance Change', y_deca_means, colors_decahose)\n",
    "create_boxplot(axs[0, 1], 'Decahose: Volume Change', y_deca_vol_means, colors_decahose)\n",
    "create_boxplot(axs[1, 0], 'Panel: Importance Change', y_panel_means, colors_panel)\n",
    "create_boxplot(axs[1, 1], 'Panel: Volume Change', y_panel_vol_means, colors_panel)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "89a59b32-ffac-4292-a0b7-1394f06bdd2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data for Decahose dataset\n",
    "categories = ['Low-Quality Content', 'High-Quality Content']\n",
    "volume_changes = [-0.9, -16.3]  # % change in volume (rounded to 1 decimal)\n",
    "market_share_changes = [17.5, -2.0]  # % change in market share (rounded to 1 decimal)\n",
    "\n",
    "# Data for Twitter Panel dataset\n",
    "volume_changes_panel = [-23.0, -26.9]  # % change in volume (rounded to 1 decimal)\n",
    "market_share_changes_panel = [6.4, -0.9]  # % change in market share (rounded to 1 decimal)\n",
    "\n",
    "# Colors for the updated plot\n",
    "colors_decahose = ['black', 'green']  # Tones of blue for Decahose\n",
    "colors_panel = ['black', 'green']  # Tones of orange for Twitter Panel\n",
    "\n",
    "# Creating a combined plot for both Twitter Panel and Decahose datasets with updated colors\n",
    "fig, axs = plt.subplots(2, 2, figsize=(12, 12), sharey=True)\n",
    "\n",
    "# Titles and data for each subplot\n",
    "datasets = ['Decahose Dataset', 'Twitter Panel Dataset']\n",
    "volume_changes_all = [volume_changes, volume_changes_panel]\n",
    "market_share_changes_all = [market_share_changes, market_share_changes_panel]\n",
    "\n",
    "# Plotting the data for each dataset with different color tones\n",
    "for i, dataset in enumerate(datasets):\n",
    "    # Volume Change subplot\n",
    "    if dataset == 'Decahose Dataset':\n",
    "        colors = colors_decahose\n",
    "    else:\n",
    "        colors = colors_panel\n",
    "\n",
    "    axs[i, 0].bar(categories, volume_changes_all[i], color=colors)\n",
    "    axs[i, 0].set_title(f'{dataset}: Volume Change (%)')\n",
    "    axs[i, 0].set_ylabel('Percentage Change')\n",
    "    axs[i, 0].set_ylim(-30, 20)\n",
    "    axs[i, 0].grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "    # Add bold y=0 line\n",
    "    axs[i, 0].axhline(0, color='black', linewidth=2)  # Bold y=0 line\n",
    "\n",
    "    # Market Share Change subplot\n",
    "    axs[i, 1].bar(categories, market_share_changes_all[i], color=colors)\n",
    "    axs[i, 1].set_title(f'{dataset}: Market Share Change (%)')\n",
    "    axs[i, 1].grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "    # Add bold y=0 line\n",
    "    axs[i, 1].axhline(0, color='black', linewidth=2)  # Bold y=0 line\n",
    "\n",
    "# Adding a main title\n",
    "fig.suptitle('Changes in Content Volume and Market Share for Twitter Panel and Decahose Datasets', fontsize=16)\n",
    "\n",
    "# Display the combined plots\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "#plt.savefig('/home/ozturan/covid-conspiracy-narratives/src/musk/submission/figures/newsguard_full_marketshare_change.png', dpi=1200, bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "82614122-4c88-4086-b387-8036cf6cccdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[np.float64(14.490580136553247), np.float64(27.907925560401715), np.float64(12.358032547184624), np.float64(-4.064932402646688)]\n",
      "[np.float64(-2.966713707556838), np.float64(9.591486860574573), np.float64(-3.3498918641170112), np.float64(-18.200476649838887)]\n"
     ]
    }
   ],
   "source": [
    "# Define custom intervals\n",
    "intervals = [(0, 39),(40, 59),(60, 74),(75, 100)]\n",
    "\n",
    "# Initialize lists for storing results\n",
    "x_deca = []\n",
    "y_deca = []\n",
    "y_deca_vol = []\n",
    "\n",
    "for interval in intervals:\n",
    "    lower, upper = interval\n",
    "    \n",
    "    # Filter dataframe based on the custom interval\n",
    "    filtered_df = merged_df[(merged_df['Score'] > lower) & (merged_df['Score'] <= upper)]\n",
    "    \n",
    "    # Calculate daily sum and ratios\n",
    "    daily_sum = filtered_df.groupby('date')['count'].sum()\n",
    "    daily_sum_all = merged_df.groupby('date')['count'].sum()\n",
    "    daily_ratio = daily_sum / daily_sum_all\n",
    "    \n",
    "    # Calculate relative changes in the interval-defined groups\n",
    "    change = (daily_sum[299:485].mean() - daily_sum[:299].mean()) / daily_sum[:299].mean()\n",
    "    change_r = (daily_ratio[299:485].mean() - daily_ratio[:299].mean()) / daily_ratio[:299].mean()\n",
    "    \n",
    "    # Append results\n",
    "    x_deca.append(f\"{lower}-{upper}\")\n",
    "    y_deca.append(change_r * 100)\n",
    "    y_deca_vol.append(change * 100)\n",
    "\n",
    "# Print results\n",
    "print(y_deca)\n",
    "print(y_deca_vol)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "91db252d-2bc8-4fe9-9afc-05190ae51555",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[np.float64(-0.5133828762590024), np.float64(30.34334335552621), np.float64(4.194735328550365), np.float64(-1.2410372714592508)]\n",
      "[np.float64(-28.17740858352895), np.float64(-4.896890581809054), np.float64(-24.70280940861409), np.float64(-26.906682891237455)]\n"
     ]
    }
   ],
   "source": [
    "# Define custom intervals\n",
    "intervals = [(0, 39),(40, 59),(60, 74),(75, 100)]\n",
    "\n",
    "\n",
    "# Initialize lists for storing results\n",
    "x = []\n",
    "y = []\n",
    "count_url_bef = []\n",
    "count_url_aft = []\n",
    "y_vol = []\n",
    "\n",
    "for interval in intervals:\n",
    "    lower, upper = interval\n",
    "    \n",
    "    # Filter dataframe based on the custom interval\n",
    "    filtered_df = p_merged_df[(p_merged_df['Score'] > lower) & (p_merged_df['Score'] <= upper)]\n",
    "    \n",
    "    # Calculate daily sums and ratios\n",
    "    daily_sum = filtered_df.groupby('date')['count'].sum()\n",
    "    daily_sum_all = p_merged_df.groupby('date')['count'].sum()\n",
    "    daily_ratio = daily_sum / daily_sum_all\n",
    "    \n",
    "    # Compute means before and after intervention and append results\n",
    "    bef_mean = daily_sum[:299].mean()\n",
    "    aft_mean = daily_sum[299:485].mean()\n",
    "    count_url_bef.append(bef_mean)\n",
    "    count_url_aft.append(aft_mean)\n",
    "    \n",
    "    # Calculate relative changes in counts and ratios\n",
    "    change = (aft_mean - bef_mean) / bef_mean\n",
    "    change_r = (daily_ratio[299:485].mean() - daily_ratio[:299].mean()) / daily_ratio[:299].mean()\n",
    "    \n",
    "    # Append results to lists\n",
    "    x.append(f\"{lower}-{upper}\")\n",
    "    y_vol.append(change * 100)\n",
    "    y.append(change_r * 100)\n",
    "\n",
    "# Print results\n",
    "print(y)\n",
    "print(y_vol)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b8ae5458-afc3-4a47-bd32-5bbbe91f73dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Adjust the color and position of value labels based on the bar color and value size\n",
    "def create_bar_plot(ax, title, data, colors, ylabel='Percentage Change', place_neg_below=False):\n",
    "    ax.bar(categories, data, color=colors)\n",
    "    ax.set_title(title)\n",
    "    ax.set_ylabel(ylabel)\n",
    "    ax.set_xlabel('Information Quality')\n",
    "    ax.set_ylim(-30, 31)\n",
    "    ax.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "    ax.axhline(0, color='black', linewidth=2)  # Bold y=0 line\n",
    "\n",
    "    # Add value labels with visibility adjustments\n",
    "    for j, (v, color) in enumerate(zip(data, colors)):\n",
    "        # Choose text color for visibility\n",
    "        text_color = 'white' if color in ['darkblue', 'mediumblue','darkorange'] else 'black'\n",
    "        \n",
    "        # Place negative values below the bar if specified\n",
    "        vertical_offset = -1.5 if place_neg_below and v < 1 else (1 if v < 0 else -3)\n",
    "        if abs(v) < 1 and not place_neg_below:\n",
    "            vertical_offset = 3 if v > 0 else -5  # Move small values away from bars if not placing negatives below\n",
    "\n",
    "        ax.text(j, v + vertical_offset, f\"{v:.1f}%\", ha='center', color=text_color, fontsize=10)\n",
    "        #vertical_offset = 1 if v < 0 else -3\n",
    "        #if abs(v) < 1:\n",
    "        #    vertical_offset = 3 if v > 0 else -2  # Move small values away from bars\n",
    "        \n",
    "# Colors for each interval in each dataset\n",
    "colors_decahose = ['darkblue', 'mediumblue', 'royalblue', 'skyblue']\n",
    "colors_panel = ['darkorange', 'orange', 'gold', 'lightcoral']\n",
    "\n",
    "# Create combined plot\n",
    "fig, axs = plt.subplots(2, 2, figsize=(12, 12), sharey=True)\n",
    "\n",
    "# Plot for Decahose dataset\n",
    "create_bar_plot(axs[0, 0], 'Decahose Dataset: Volume Change (%)', y_deca_vol, colors_decahose)\n",
    "create_bar_plot(axs[0, 1], 'Decahose Dataset: Market Share Change (%)', y_deca, colors_decahose)\n",
    "\n",
    "# Plot for Twitter Panel dataset\n",
    "create_bar_plot(axs[1, 0], 'Twitter Panel Dataset: Volume Change (%)', y_vol, colors_panel)\n",
    "create_bar_plot(axs[1, 1], 'Twitter Panel Dataset: Market Share Change (%)', y, colors_panel, place_neg_below=True)\n",
    "# Manually add label for the 0-39 Twitter Panel market share change value\n",
    "axs[1, 1].text(0, -0.5133828762590024 - 1.5, f\"{-0.5133828762590024:.1f}%\", ha='center', color='black', fontsize=10)\n",
    "\n",
    "# Adding a main title\n",
    "fig.suptitle('Changes in Content Volume and Market Share for Twitter Panel and Decahose Datasets', fontsize=16)\n",
    "\n",
    "# Display the combined plots\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "plt.rcParams.update({'font.size': 12})\n",
    "\n",
    "#plt.savefig('/home/ozturan/covid-conspiracy-narratives/src/musk/submission/figures/newsguard_marketshare_4bins_change.png', dpi=1200, bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cf5429b4-1efb-4a25-b962-e7d70b28e908",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define custom intervals\n",
    "intervals = [(0, 39), (40, 59), (60, 74), (75, 100)]\n",
    "\n",
    "# Number of bootstrap samples\n",
    "n_bootstrap = 10\n",
    "\n",
    "# Initialize lists for storing bootstrapped results\n",
    "y_bootstrap = []\n",
    "y_vol_bootstrap = []\n",
    "y_deca_bootstrap = []\n",
    "y_deca_vol_bootstrap = []\n",
    "\n",
    "# Function for bootstrapping\n",
    "def bootstrap_ci(data, n_bootstrap=n_bootstrap, ci=95):\n",
    "    boot_means = [np.mean(np.random.choice(data, size=len(data), replace=True)) for _ in range(n_bootstrap)]\n",
    "    lower_bound = np.percentile(boot_means, (100 - ci) / 2)\n",
    "    upper_bound = np.percentile(boot_means, 100 - (100 - ci) / 2)\n",
    "    return np.mean(boot_means), lower_bound, upper_bound\n",
    "\n",
    "# Perform bootstrapping for each interval in p_merged_df (Panel Data)\n",
    "for interval in intervals:\n",
    "    lower, upper = interval\n",
    "    filtered_df = p_merged_df[(p_merged_df['Score'] > lower) & (p_merged_df['Score'] <= upper)]\n",
    "    \n",
    "    daily_sum = filtered_df.groupby('date')['count'].sum()\n",
    "    daily_sum_all = p_merged_df.groupby('date')['count'].sum()\n",
    "    daily_ratio = daily_sum / daily_sum_all\n",
    "\n",
    "    change_bootstrap = []\n",
    "    change_r_bootstrap = []\n",
    "\n",
    "    for _ in range(n_bootstrap):\n",
    "        # Separate bootstrapping for before and after periods\n",
    "        sampled_indices_bef = np.random.choice(daily_sum.index[:299], size=299, replace=True)\n",
    "        sampled_indices_aft = np.random.choice(daily_sum.index[299:485], size=(485 - 299), replace=True)\n",
    "\n",
    "        sampled_daily_sum_bef = daily_sum.loc[sampled_indices_bef]\n",
    "        sampled_daily_sum_aft = daily_sum.loc[sampled_indices_aft]\n",
    "\n",
    "        sampled_daily_ratio_bef = daily_ratio.loc[sampled_indices_bef]\n",
    "        sampled_daily_ratio_aft = daily_ratio.loc[sampled_indices_aft]\n",
    "\n",
    "        bef_mean = sampled_daily_sum_bef.mean()\n",
    "        aft_mean = sampled_daily_sum_aft.mean()\n",
    "        change = (aft_mean - bef_mean) / bef_mean\n",
    "\n",
    "        bef_ratio_mean = sampled_daily_ratio_bef.mean()\n",
    "        aft_ratio_mean = sampled_daily_ratio_aft.mean()\n",
    "        change_r = (aft_ratio_mean - bef_ratio_mean) / bef_ratio_mean\n",
    "\n",
    "        change_bootstrap.append(change * 100)\n",
    "        change_r_bootstrap.append(change_r * 100)\n",
    "\n",
    "    y_bootstrap.append(bootstrap_ci(change_r_bootstrap))\n",
    "    y_vol_bootstrap.append(bootstrap_ci(change_bootstrap))\n",
    "\n",
    "# Perform bootstrapping for each interval in merged_df (Decahose Data)\n",
    "for interval in intervals:\n",
    "    lower, upper = interval\n",
    "    filtered_df = merged_df[(merged_df['Score'] > lower) & (merged_df['Score'] <= upper)]\n",
    "    \n",
    "    daily_sum = filtered_df.groupby('date')['count'].sum()\n",
    "    daily_sum_all = merged_df.groupby('date')['count'].sum()\n",
    "    daily_ratio = daily_sum / daily_sum_all\n",
    "\n",
    "    change_bootstrap = []\n",
    "    change_r_bootstrap = []\n",
    "\n",
    "    for _ in range(n_bootstrap):\n",
    "        # Separate bootstrapping for before and after periods\n",
    "        sampled_indices_bef = np.random.choice(daily_sum.index[:299], size=299, replace=True)\n",
    "        sampled_indices_aft = np.random.choice(daily_sum.index[299:485], size=(485 - 299), replace=True)\n",
    "\n",
    "        sampled_daily_sum_bef = daily_sum.loc[sampled_indices_bef]\n",
    "        sampled_daily_sum_aft = daily_sum.loc[sampled_indices_aft]\n",
    "\n",
    "        sampled_daily_ratio_bef = daily_ratio.loc[sampled_indices_bef]\n",
    "        sampled_daily_ratio_aft = daily_ratio.loc[sampled_indices_aft]\n",
    "\n",
    "        bef_mean = sampled_daily_sum_bef.mean()\n",
    "        aft_mean = sampled_daily_sum_aft.mean()\n",
    "        change = (aft_mean - bef_mean) / bef_mean\n",
    "\n",
    "        bef_ratio_mean = sampled_daily_ratio_bef.mean()\n",
    "        aft_ratio_mean = sampled_daily_ratio_aft.mean()\n",
    "        change_r = (aft_ratio_mean - bef_ratio_mean) / bef_ratio_mean\n",
    "\n",
    "        change_bootstrap.append(change * 100)\n",
    "        change_r_bootstrap.append(change_r * 100)\n",
    "\n",
    "    y_deca_bootstrap.append(bootstrap_ci(change_r_bootstrap))\n",
    "    y_deca_vol_bootstrap.append(bootstrap_ci(change_bootstrap))\n",
    "\n",
    "# Extract means and confidence intervals\n",
    "def extract_ci(bootstrap_results):\n",
    "    return (\n",
    "        [b[0] for b in bootstrap_results],  # Mean values\n",
    "        [b[1] for b in bootstrap_results],  # Lower bound\n",
    "        [b[2] for b in bootstrap_results]   # Upper bound\n",
    "    )\n",
    "\n",
    "y_mean, y_lower, y_upper = extract_ci(y_bootstrap)\n",
    "y_vol_mean, y_vol_lower, y_vol_upper = extract_ci(y_vol_bootstrap)\n",
    "y_deca_mean, y_deca_lower, y_deca_upper = extract_ci(y_deca_bootstrap)\n",
    "y_deca_vol_mean, y_deca_vol_lower, y_deca_vol_upper = extract_ci(y_deca_vol_bootstrap)\n",
    "\n",
    "# Categories for plotting\n",
    "categories = [\"0-39\", \"40-59\", \"60-74\", \"75-100\"]\n",
    "colors_decahose = ['darkblue', 'mediumblue', 'royalblue', 'skyblue']\n",
    "colors_panel = ['darkorange', 'orange', 'gold', 'lightcoral']\n",
    "\n",
    "# Function to create bar plots with bootstrapped confidence intervals\n",
    "def create_bar_plot_bootstrap(ax, title, data_mean, data_ci_lower, data_ci_upper, colors, ylabel='Percentage Change'):\n",
    "    x_positions = np.arange(len(data_mean))\n",
    "    \n",
    "    yerr = [np.abs(np.array(data_mean) - np.array(data_ci_lower)), np.abs(np.array(data_ci_upper) - np.array(data_mean))]\n",
    "\n",
    "    ax.bar(x_positions, data_mean, color=colors, yerr=yerr, capsize=5)\n",
    "    ax.set_title(title)\n",
    "    ax.set_ylabel(ylabel)\n",
    "    ax.set_xlabel('Information Quality')\n",
    "    ax.set_xticks(x_positions)\n",
    "    ax.set_xticklabels(categories, rotation=45, ha='right')\n",
    "    ax.set_ylim(-30, 31)\n",
    "    ax.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "    ax.axhline(0, color='black', linewidth=2)\n",
    "\n",
    "    for j, (v, color) in enumerate(zip(data_mean, colors)):\n",
    "        text_color = 'white' if color in ['darkblue', 'mediumblue', 'darkorange'] else 'black'\n",
    "        ax.text(j, v - 2, f\"{v:.1f}%\", ha='center', color=text_color, fontsize=10)\n",
    "\n",
    "# Create combined plot with bootstrapped confidence intervals\n",
    "fig, axs = plt.subplots(2, 2, figsize=(12, 12), sharey=True)\n",
    "\n",
    "# Plot for Decahose dataset\n",
    "create_bar_plot_bootstrap(axs[0, 0], 'Decahose Dataset: Volume Change (%)', y_deca_vol_mean, y_deca_vol_lower, y_deca_vol_upper, colors_decahose)\n",
    "create_bar_plot_bootstrap(axs[0, 1], 'Decahose Dataset: Market Share Change (%)', y_deca_mean, y_deca_lower, y_deca_upper, colors_decahose)\n",
    "\n",
    "# Plot for Twitter Panel dataset\n",
    "create_bar_plot_bootstrap(axs[1, 0], 'Twitter Panel Dataset: Volume Change (%)', y_vol_mean, y_vol_lower, y_vol_upper, colors_panel)\n",
    "create_bar_plot_bootstrap(axs[1, 1], 'Twitter Panel Dataset: Market Share Change (%)', y_mean, y_lower, y_upper, colors_panel)\n",
    "\n",
    "# Adding a main title\n",
    "fig.suptitle('Bootstrapped Changes in Content Volume and Market Share for Twitter Panel and Decahose Datasets', fontsize=16)\n",
    "\n",
    "# Display the combined plots\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "plt.rcParams.update({'font.size': 12})\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e41b4a18-a015-43cc-a1eb-255be9c083a7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x2000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a larger figure for better spacing\n",
    "fig, axs = plt.subplots(2, 2, figsize=(14, 20), sharey=True)\n",
    "\n",
    "# Categories for plotting\n",
    "categories = [\"0-39\", \"40-59\", \"60-74\", \"75-100\"]\n",
    "\n",
    "# Colors for each dataset\n",
    "colors_decahose = ['darkblue', 'darkblue', 'darkblue', 'darkblue']\n",
    "colors_panel = ['gold', 'gold', 'gold', 'gold']\n",
    "\n",
    "# Function to create box plots\n",
    "def create_boxplot(ax, title, data, colors, ylabel='Percentage Change'):\n",
    "    box = ax.boxplot(data, patch_artist=True, medianprops=dict(color='black'), widths=0.6)\n",
    "    ax.set_title(title, fontsize=14)\n",
    "    ax.set_ylabel(ylabel, fontsize=12)\n",
    "    ax.set_xlabel('Information Quality', fontsize=12)\n",
    "    ax.set_xticks(range(1, len(categories) + 1))  # Ensure x-ticks match the number of categories\n",
    "    ax.set_xticklabels(categories, rotation=0, ha='right', fontsize=11)\n",
    "    ax.set_ylim(-30, 35)\n",
    "    ax.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "    ax.axhline(0, color='black', linewidth=2)  # Bold y=0 line\n",
    "\n",
    "    # Color each box according to dataset\n",
    "    for patch, color in zip(box['boxes'], colors):\n",
    "        patch.set_facecolor(color)\n",
    "    \n",
    "# Convert bootstrapped lists to a list of lists format for boxplot\n",
    "y_deca_vol_bootstrap_list = [list(values) for values in y_deca_vol_bootstrap]\n",
    "y_deca_bootstrap_list = [list(values) for values in y_deca_bootstrap]\n",
    "y_vol_bootstrap_list = [list(values) for values in y_vol_bootstrap]\n",
    "y_bootstrap_list = [list(values) for values in y_bootstrap]\n",
    "\n",
    "# Plot for Decahose dataset\n",
    "create_boxplot(axs[0, 0], 'Decahose Dataset: Volume Change (%)', y_deca_vol_bootstrap_list, colors_decahose)\n",
    "create_boxplot(axs[0, 1], 'Decahose Dataset: Market Share Change (%)', y_deca_bootstrap_list, colors_decahose)\n",
    "\n",
    "# Plot for Twitter Panel dataset\n",
    "create_boxplot(axs[1, 0], 'Twitter Panel Dataset: Volume Change (%)', y_vol_bootstrap_list, colors_panel)\n",
    "create_boxplot(axs[1, 1], 'Twitter Panel Dataset: Market Share Change (%)', y_bootstrap_list, colors_panel)\n",
    "\n",
    "# Adjust spacing between subplots\n",
    "fig.subplots_adjust(hspace=0.3, wspace=0.3)\n",
    "\n",
    "# Adding a main title with more spacing\n",
    "#fig.suptitle('Bootstrapped Changes in Content Volume and Market Share for Twitter Panel and Decahose Datasets', fontsize=16, y=1.02)\n",
    "plt.savefig('/home/ozturan/covid-conspiracy-narratives/src/musk/submission/figures/fig2_bootstrapped_ci.png',dpi=1200,bbox_inches='tight')\n",
    "\n",
    "# Display the combined plots\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "63e13eae-8bfb-45d0-bcce-bffb68764ac8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a figure with an optimized size for Google Docs\n",
    "fig, axs = plt.subplots(2, 2, figsize=(10, 12), sharey=True)\n",
    "\n",
    "# Categories for plotting\n",
    "categories = [\"0-39\", \"40-59\", \"60-74\", \"75-100\"]\n",
    "\n",
    "# Colors for each dataset\n",
    "# Colors for each dataset\n",
    "colors_decahose = ['darkblue', 'darkblue', 'darkblue', 'darkblue']\n",
    "colors_panel = ['gold', 'gold', 'gold', 'gold']\n",
    "\n",
    "\n",
    "# Function to create box plots\n",
    "def create_boxplot(ax, title, data, colors, ylabel='Percentage Change'):\n",
    "    box = ax.boxplot(data, patch_artist=True, medianprops=dict(color='black'), widths=0.6)\n",
    "    \n",
    "    ax.set_title(title, fontsize=16)\n",
    "    ax.set_ylabel(ylabel, fontsize=14)\n",
    "    ax.set_xlabel('Information Quality', fontsize=14)\n",
    "    \n",
    "    ax.set_xticks(range(1, len(categories) + 1))  # Ensure x-ticks match the number of categories\n",
    "    ax.set_xticklabels(categories, rotation=0, fontsize=12)\n",
    "    \n",
    "    ax.set_ylim(-30, 35)\n",
    "    ax.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "    ax.axhline(0, color='black', linewidth=2)  # Bold y=0 line\n",
    "\n",
    "    # Color each box according to dataset\n",
    "    for patch, color in zip(box['boxes'], colors):\n",
    "        patch.set_facecolor(color)\n",
    "\n",
    "# Convert bootstrapped lists to a list of lists format for boxplot\n",
    "y_deca_vol_bootstrap_list = [list(values) for values in y_deca_vol_bootstrap]\n",
    "y_deca_bootstrap_list = [list(values) for values in y_deca_bootstrap]\n",
    "y_vol_bootstrap_list = [list(values) for values in y_vol_bootstrap]\n",
    "y_bootstrap_list = [list(values) for values in y_bootstrap]\n",
    "\n",
    "# Plot for Decahose dataset\n",
    "create_boxplot(axs[0, 0], 'Decahose: Volume Change (%)', y_deca_vol_bootstrap_list, colors_decahose)\n",
    "create_boxplot(axs[0, 1], 'Decahose: Market Share Change (%)', y_deca_bootstrap_list, colors_decahose)\n",
    "\n",
    "# Plot for Twitter Panel dataset\n",
    "create_boxplot(axs[1, 0], 'Twitter Panel: Volume Change (%)', y_vol_bootstrap_list, colors_panel)\n",
    "create_boxplot(axs[1, 1], 'Twitter Panel: Market Share Change (%)', y_bootstrap_list, colors_panel)\n",
    "\n",
    "# Adjust spacing between subplots for better readability\n",
    "#fig.subplots_adjust(hspace=0.5, wspace=0.4)\n",
    "\n",
    "# Apply tight layout to optimize spacing\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save as high-resolution PNG and PDF for Google Docs\n",
    "plt.savefig('/home/ozturan/covid-conspiracy-narratives/src/musk/submission/figures/fig2_bootstrapped_ci.png', dpi=600, bbox_inches='tight', pad_inches=0.1)\n",
    "plt.savefig('/home/ozturan/covid-conspiracy-narratives/src/musk/submission/figures/fig2_bootstrapped_ci.pdf', dpi=600, bbox_inches='tight', pad_inches=0.1, format='pdf')\n",
    "\n",
    "# Display the combined plots\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "96901254-8d23-4657-9111-7274eaa7cf87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(-3.3471181216385855)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(y_deca_vol_bootstrap_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "346f6f4d-5106-4034-be3d-352fb9689252",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[np.float64(-2.803363475473243),\n",
       "  np.float64(-3.6608116983541237),\n",
       "  np.float64(-1.822952726372126)],\n",
       " [np.float64(10.694245080517224),\n",
       "  np.float64(9.151634440185198),\n",
       "  np.float64(13.43978647312475)],\n",
       " [np.float64(-3.791342830953936),\n",
       "  np.float64(-4.267267823170709),\n",
       "  np.float64(-3.224348660614825)],\n",
       " [np.float64(-17.70008098245723),\n",
       "  np.float64(-18.279912372961093),\n",
       "  np.float64(-17.18352364206177)]]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_deca_vol_bootstrap_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "299731eb-c5ab-49b3-8d4b-312add7d81b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[np.float64(15.121313099938359),\n",
       "  np.float64(14.720062820052675),\n",
       "  np.float64(15.491234120163815)],\n",
       " [np.float64(29.426210785491037),\n",
       "  np.float64(28.108664383263914),\n",
       "  np.float64(30.861157783495127)],\n",
       " [np.float64(12.034892106328687),\n",
       "  np.float64(11.069982472268396),\n",
       "  np.float64(12.697995297218737)],\n",
       " [np.float64(-4.061395770656143),\n",
       "  np.float64(-4.316880167968948),\n",
       "  np.float64(-3.950229474010602)]]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_deca_bootstrap_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "2f13fd59-5e73-4268-aa43-2d0c2d6ef895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[np.float64(-28.6085430701818),\n",
       "  np.float64(-29.662762449886134),\n",
       "  np.float64(-28.018429752380367)],\n",
       " [np.float64(-4.4763731015079244),\n",
       "  np.float64(-6.2335233831756245),\n",
       "  np.float64(-3.3816659361925425)],\n",
       " [np.float64(-24.92580665020174),\n",
       "  np.float64(-25.82523319133445),\n",
       "  np.float64(-24.094229160890418)],\n",
       " [np.float64(-25.920432115602544),\n",
       "  np.float64(-26.96591975457223),\n",
       "  np.float64(-24.97679742253577)]]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "y_vol_bootstrap_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "69f2427f-3ee5-4152-b1aa-70e4eefa9104",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_bootstrap_list' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43my_bootstrap_list\u001b[49m \n",
      "\u001b[0;31mNameError\u001b[0m: name 'y_bootstrap_list' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "y_bootstrap_list "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "580bed07-4f79-4d74-a243-0ebb68b04b29",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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